ChromHMM1
Learning and analysis chromatin states using a multivariate hidden markov model
Usage
java -Xmx
[GB]
g
-jar
/usr/share/java/chromhmm.jar
[options]
Overview
ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.
Options
LearnModel-
Takes a set of binarized data files, learns chromatin state models, and by default produces a segmentation, generates browser output with default settings, and calls OverlapEnrichment and NeighborhoodEnrichments with default settings for the specified genome assembly. A webpage is a created with links to all the files and images created.
BinarizeBed-
Converts a set of bed files of aligned reads into binarized data files for model learning and optionally prints the intermediate signal files.
BinarizeBam-
Converts a set of bam files of aligned reads into binarized data files for model learning and optionally prints the intermediate signal files.
BinarizeSignal-
Converts a set of signal files into binarized files.
MakeSegmentation-
Takes a learned model and binarized data and outputs a segmentation.
MakeBrowserFiles-
Can convert segmentation files into a browser viewable format.
OverlapEnrichment-
Shows the enrichment of each state of a segmentation for a set of external data.
NeighborhoodEnrichment-
Shows the enrichment of each state relative to a set of anchor positions.
CompareModels-
Can compare models with different numbers of states in terms of correlation in emission parameters.
Reorder-
Allows reordering the states of the model, the columns of the emission matrix, or adding state labels.
EvalSubset-
Can be used to evaluate the extent to which a subset of marks can recover a segmentation using the full set of marks.
StatePruning-
Can be used to prune states from a model in order to initialize models when using the non-default two pass approach.